{"id":3450256,"url":"https://alion.io/job/pearson-senior-data-engineer","title":"Senior Data Engineer","company":{"id":34029,"name":"Pearson","domain":"pearson.com","url":"https://alion.io/company/pearson","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":76,"open_postings":19,"ghost_share":0,"stale_share":0.947,"repost_share":0,"time_to_fill_p50_days":26,"computed_at":"2026-11-02T06:13:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sri Lanka"],"countries":["LK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13000,"max_usd":35000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Collibra","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Looker","optional":false},{"name":"LookML","optional":false},{"name":"Power BI","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Alation","optional":true},{"name":"Claude Code","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-10-29T06:00:00Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-02T06:00:33Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Job Title: Senior Data Engineer\nAbout the role\nPearson Virtual Schools runs online K-12 programs, including Connections Academy and Pearson Online Academy, for students and families across the country. Everyone who works on those programs depends on data they can trust: educators tracking student progress, the teams improving our curriculum, and more and more, AI tools working alongside them.\nWe're starting a new Data Engineering group, and this is one of its first two hires. You'll build our governed semantic layer from scratch. It will be the single, trusted definition of our data and metrics, used both by BI tools and by AI agents. You'll begin with two high-impact domains, student assessment and learning efficacy and marketing / commercial, and the modeling standards you set will shape how the group grows.\nThis is a greenfield role with a lot of autonomy. You'll pick the patterns, write the conventions, and help the engineers who join after you.\nWhat you'll do\nModel the data. Design and build dbt models on BigQuery across raw, conformed, and serving layers for our first two domains.\nBuild the semantic layer. Create and maintain LookML explores, views, and measures as the reusable contract that every dashboard, analyst, and AI agent queries.\nMake the numbers trustworthy. Put dbt tests, documentation, and reconciliation against established reporting sources in place, so educators and data scientists can rely on the results.\nCatalog and classify. Register and classify data assets in Collibra, including student data that is sensitive under FERPA.\nDesign for secure access. Work closely with our Data Platform Engineer (Security & AI Access) so every model works with per-user, per-location, and purpose-based access controls.\nSet the standard. Document models and conventions, define how the team models data, and mentor the peers who follow.\nWhat you'll bring\nStrong SQL and hands-on dbt experience: ELT and medallion patterns, testing, documentation, and Git-based workflows.\nProven data modeling across dimensional and wide / denormalized designs, with disciplined metric definitions.\nExperience building a semantic or metrics layer. LookML / Looker is ideal, and experience with the dbt Semantic Layer, Cube, or Power BI semantic models also counts.\nProduction experience on BigQuery or another cloud data warehouse (Snowflake, Redshift, Databricks).\nComfort turning loosely scoped problems into production-ready models, and setting patterns others can follow.\nUsually 6+ years in data engineering, analytics engineering, or a related field.\nNice to have\nCollibra or a similar catalog / governance tool (Alation, Microsoft Purview).\nPython.\nExperience with AI-assisted engineering tools such as Claude Code.\nExperience with education or assessment data, or with FERPA or other regulated data.\nDon't tick every box? Apply anyway. We care more about strong modeling fundamentals and curiosity than an exact match to our stack.\nWhy you'll love it\nBuild from zero. You'll design the foundation instead of inheriting legacy code.\nReal impact. Your work helps educators understand how students are learning.\nAI-ready by design. You'll build a semantic layer that people and AI agents use the same way, under the same rules.\nShape a new team. 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